The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are:
* What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images?
* How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians?
Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians.
Participants (physicians acting as clinical readers) will:
* Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive.
* Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results.
* Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.
Who can participate
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Inclusion Criteria:
* Patients who have provided informed consent for the use of their clinical images in scientific research.
* Clinical images with a resolution exceeding 224x224 pixels, ensuring compatibility with the artificial intelligence architecture.
* Retrospective records of solitary skin lesions with confirmed diagnoses.
Exclusion Criteria:
* Patients who have not consented to the use of their clinical photographs for research purposes.
* Images containing potentially identifiable personal information or visual features that compromise patient anonymity.
* Images with a resolution lower than 224x224 pixels or poor diagnostic quality (e.g., blurring, significant occlusion).
* Duplicate images or entries for the same lesion.
Questions worth asking your doctor
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
Questions for the trial coordinator
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
What they're measuring
1
Diagnostic accuracy of the CNN-based artificial intelligence model
Timeframe: Baseline (Retrospective data analysis will be completed within 4 months)